公开(公告)号：US11900222B1

公开(公告)日：2024-02-13

申请号：US16355185

申请日：2019-03-15

Applicant: Google LLC

Inventor： Jyrki A. Alakuijala ,  Quentin Lascombes de Laroussilhe ,  Andrey Khorlin ,  Jeremiah Joseph Harmsen ,  Andrea Gesmundo

IPC: G06N20/00 ,  G06N3/08

CPC classification number: G06N20/00 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for providing a machine learning model that is trained to perform a machine learning task. In one aspect, a method comprises receiving a request to train a machine learning model on a set of training examples; determining a set of one or more meta-data values characterizing the set of training examples; using a mapping function to map the set of meta-data values characterizing the set of training examples to data identifying a particular machine learning model architecture; selecting, using the particular machine learning model architecture, a final machine learning model architecture for performing the machine learning task; and training a machine learning model having the final machine learning model architecture on the set of training examples.

公开(公告)号：US20240104394A1

公开(公告)日：2024-03-28

申请号：US18012387

申请日：2022-03-11

Applicant: Google LLC

Inventor： Amy Skerry-Ryan ,  Quentin Lascombes de Laroussilhe ,  Ronald Rong Yang ,  Carla Marie Riggi ,  Chansoo Lee ,  Jordan Arthur Grimstad ,  Christopher Mark Lamb ,  Joseph Michael Moran ,  Nihesh Anderson Klutto Milleth ,  Noah Weston Hadfield-Menell ,  Volodymyr Shtenovych ,  Ziqi Huang ,  Sagi Perel ,  Michael David Gerard ,  Mehadi Seid Hassen

IPC: G06N3/10 ,  G06N3/045 ,  G06N3/08

CPC classification number: G06N3/10 ,  G06N3/045 ,  G06N3/08

Abstract: Provided are computing systems, methods, and platforms that automatically produce production-ready machine learning models and deployment pipelines from minimal input information such as a raw training dataset. In particular, one example computing system can import a training dataset associated with a user. The computing system can execute an origination machine learning pipeline to perform a model architecture search that selects and trains a machine learning model for the training dataset. Execution of the origination machine learning pipeline can also result in generation of a deployment machine learning pipeline configured to enable deployment of the machine learning model (e.g., running the machine learning model to produce inferences and/or optionally other tasks such as re-training and/or re-tuning the model). The computing system can export the machine learning model and the deployment machine learning pipeline for deployment of the machine learning model with the deployment machine learning pipeline

公开(公告)号：US20220092416A1

公开(公告)日：2022-03-24

申请号：US17418555

申请日：2019-12-27

Applicant: Google LLC

Inventor： Neil Matthew Tinmouth Houlsby ,  Quentin Lascombes de Laroussilhe ,  Stanislaw Kamil Jastrzebski ,  Andrea Gesmundo

IPC: G06N3/08 ,  G06N3/04

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining neural network architectures. One of the methods includes receiving training data for training a task neural network to perform a particular machine learning task; and selecting, from a space of possible architectures, an architecture for the task neural network, wherein the space of possible architectures is represented as a graph of nodes connected by edges, each node in the graph representing a decision point in selecting the architecture and each edge in the graph representing an action.

公开(公告)号：US10791078B2

公开(公告)日：2020-09-29

申请号：US15953266

申请日：2018-04-13

Applicant: Google LLC

Inventor： Fredrik Bergenlid ,  Vladyslav Lysychkin ,  Denis Burakov ,  Behshad Behzadi ,  Andrea Terwisscha van Scheltinga ,  Quentin Lascombes de Laroussilhe ,  Mikhail Golikov ,  Koa Metter ,  Ibrahim Badr ,  Zaheed Sabur

IPC: H04L12/58 ,  G06F16/44 ,  H04N21/4788 ,  H04N21/439 ,  H04N7/15 ,  G10L15/22 ,  G10L25/63 ,  G10L15/16 ,  G10L15/00

Abstract: Implementations relate to providing information items for display during a communication session. In some implementations, a computer-implemented method includes receiving, during a communication session between a first computing device and a second computing device, first media content from the communication session. The method further includes determining a first information item for display in the communication session based at least in part on the first media content. The method further includes sending a first command to at least one of the first computing device and the second computing device to display the first information item.

公开(公告)号：US20190036856A1

公开(公告)日：2019-01-31

申请号：US15953266

申请日：2018-04-13

Applicant: Google LLC

Inventor： Fredrik Bergenlid ,  Vladyslav Lysychkin ,  Denis Burakov ,  Behshad Behzadi ,  Andrea Terwisscha van Scheltinga ,  Quentin Lascombes de Laroussilhe ,  Mikhail Golikov ,  Koa Metter ,  Ibrahim Badr ,  Zaheed Sabur

IPC: H04L12/58 ,  H04N7/15 ,  G06F17/30

CPC classification number: H04L51/10 ,  G06F16/44 ,  G10L15/005 ,  G10L15/16 ,  G10L15/22 ,  G10L25/63 ,  G10L2015/223 ,  H04N7/15 ,  H04N21/4394 ,  H04N21/4788

Abstract: Implementations relate to providing information items for display during a communication session. In some implementations, a computer-implemented method includes receiving, during a communication session between a first computing device and a second computing device, first media content from the communication session. The method further includes determining a first information item for display in the communication session based at least in part on the first media content. The method further includes sending a first command to at least one of the first computing device and the second computing device to display the first information item.